
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowercase    :   Any						=			2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowercase    :   Dict						=			{
    # fairseq:
    'wmt19-ru-en': {'length_penalty': 1.1},
    'wmt19-en-ru': {'length_penalty': 1.1_5},
    'wmt19-en-de': {'length_penalty': 1.0},
    'wmt19-de-en': {'length_penalty': 1.1},
    # allenai:
    'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
    'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
    'wmt16-en-de-12-1': {'length_penalty': 0.8},
    'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
    'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}

# this remaps the different models to their organization names
lowercase    :   Dict						=			{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		lowercase    :   Tuple						=			'facebook'
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
		lowercase    :   int						=			'allenai'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Tuple:






							'''simple docstring'''





							__UpperCamelCase      :  Tuple       =   dict((re.sub(R"@@$"		,    ""		,    _lowerCamelCase), v) if k.endswith("@@") else (re.sub(R"$"		,    "</w>"		,    _lowerCamelCase), v) for k, v in d.items())
							__UpperCamelCase      :  Optional[Any]       =   "<s> <pad> </s> <unk>".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														__UpperCamelCase      :  Optional[int]       =   d[k]  # restore
							return da




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Any)   ->       str:






							'''simple docstring'''





							assert os.path.exists(_lowerCamelCase)
							os.makedirs(_lowerCamelCase		,    exist_ok=_lowerCamelCase)
							print(F'Writing results to {pytorch_dump_folder_path}')

							# handle various types of models

							__UpperCamelCase      :  List[str]       =   basename(_lowerCamelCase)
							__UpperCamelCase      :  Any       =   dirname(_lowerCamelCase)

							__UpperCamelCase      :  Optional[int]       =   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							__UpperCamelCase      :  Any       =   cls.hub_models()
							__UpperCamelCase      :  List[Any]       =   {"bpe": "fastbpe", "tokenizer": "moses"}
							__UpperCamelCase      :  Optional[int]       =   "."
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F'using checkpoint {checkpoint_file}')
							__UpperCamelCase      :  Union[str, Any]       =   hub_utils.from_pretrained(
							    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    archive_map=_lowerCamelCase		,    **_lowerCamelCase)

							__UpperCamelCase      :  Tuple       =   vars(chkpt["args"]["model"])

							__UpperCamelCase      :  Union[str, Any]       =   args["source_lang"]
							__UpperCamelCase      :  List[Any]       =   args["target_lang"]

							__UpperCamelCase      :  Optional[Any]       =   dirname(_lowerCamelCase)
							__UpperCamelCase      :  Optional[Any]       =   basename(_lowerCamelCase)

							# dicts
							__UpperCamelCase      :  Any       =   os.path.join(_lowerCamelCase		,    F'dict.{src_lang}.txt')
							__UpperCamelCase      :  Tuple       =   os.path.join(_lowerCamelCase		,    F'dict.{tgt_lang}.txt')

							__UpperCamelCase      :  Dict       =   Dictionary.load(_lowerCamelCase)
							__UpperCamelCase      :  Any       =   rewrite_dict_keys(src_dict.indices)
							__UpperCamelCase      :  Any       =   len(_lowerCamelCase)
							__UpperCamelCase      :  Any       =   os.path.join(_lowerCamelCase		,    "vocab-src.json")
							print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records')
							with open(_lowerCamelCase		,    "w"		,    encoding="utf-8") as f:
														f.write(json.dumps(_lowerCamelCase		,    ensure_ascii=_lowerCamelCase		,    indent=_lowerCamelCase))

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							__UpperCamelCase      :  Any       =   True
							for k in src_vocab.keys():
														if not k.islower():
																					__UpperCamelCase      :  Optional[int]       =   False
																					break

							__UpperCamelCase      :  Optional[int]       =   Dictionary.load(_lowerCamelCase)
							__UpperCamelCase      :  Union[str, Any]       =   rewrite_dict_keys(tgt_dict.indices)
							__UpperCamelCase      :  int       =   len(_lowerCamelCase)
							__UpperCamelCase      :  int       =   os.path.join(_lowerCamelCase		,    "vocab-tgt.json")
							print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records')
							with open(_lowerCamelCase		,    "w"		,    encoding="utf-8") as f:
														f.write(json.dumps(_lowerCamelCase		,    ensure_ascii=_lowerCamelCase		,    indent=_lowerCamelCase))

							# merges_file (bpecodes)
							__UpperCamelCase      :  int       =   os.path.join(_lowerCamelCase		,    VOCAB_FILES_NAMES["merges_file"])
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														__UpperCamelCase      :  Dict       =   os.path.join(_lowerCamelCase		,    _lowerCamelCase)
														if os.path.exists(_lowerCamelCase):
																					break
							with open(_lowerCamelCase		,    encoding="utf-8") as fin:
														__UpperCamelCase      :  List[str]       =   fin.read()
							__UpperCamelCase      :  Dict       =   re.sub(R" \d+$"		,    ""		,    _lowerCamelCase		,    0		,    re.M)  # remove frequency number
							print(F'Generating {merges_file}')
							with open(_lowerCamelCase		,    "w"		,    encoding="utf-8") as fout:
														fout.write(_lowerCamelCase)

							# model config
							__UpperCamelCase      :  Optional[Any]       =   os.path.join(_lowerCamelCase		,    "config.json")

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
							assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

							__UpperCamelCase      :  List[str]       =   {
							    "architectures": ["FSMTForConditionalGeneration"],
							    "model_type": "fsmt",
							    "activation_dropout": args["activation_dropout"],
							    "activation_function": "relu",
							    "attention_dropout": args["attention_dropout"],
							    "d_model": args["decoder_embed_dim"],
							    "dropout": args["dropout"],
							    "init_std": 0.0_2,
							    "max_position_embeddings": args["max_source_positions"],
							    "num_hidden_layers": args["encoder_layers"],
							    "src_vocab_size": src_vocab_size,
							    "tgt_vocab_size": tgt_vocab_size,
							    "langs": [src_lang, tgt_lang],
							    "encoder_attention_heads": args["encoder_attention_heads"],
							    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
							    "encoder_layerdrop": args["encoder_layerdrop"],
							    "encoder_layers": args["encoder_layers"],
							    "decoder_attention_heads": args["decoder_attention_heads"],
							    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
							    "decoder_layerdrop": args["decoder_layerdrop"],
							    "decoder_layers": args["decoder_layers"],
							    "bos_token_id": 0,
							    "pad_token_id": 1,
							    "eos_token_id": 2,
							    "is_encoder_decoder": True,
							    "scale_embedding": not args["no_scale_embedding"],
							    "tie_word_embeddings": args["share_all_embeddings"],
							}

							# good hparam defaults to start with
							__UpperCamelCase      :  Any       =   5
							__UpperCamelCase      :  List[str]       =   False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__UpperCamelCase      :  Optional[Any]       =   best_score_hparams[model_dir]["length_penalty"]
							else:
														__UpperCamelCase      :  List[str]       =   1.0

							print(F'Generating {fsmt_model_config_file}')
							with open(_lowerCamelCase		,    "w"		,    encoding="utf-8") as f:
														f.write(json.dumps(_lowerCamelCase		,    ensure_ascii=_lowerCamelCase		,    indent=_lowerCamelCase))

							# tokenizer config
							__UpperCamelCase      :  Dict       =   os.path.join(_lowerCamelCase		,    _lowerCamelCase)

							__UpperCamelCase      :  Any       =   {
							    "langs": [src_lang, tgt_lang],
							    "model_max_length": 1_024,
							    "do_lower_case": do_lower_case,
							}

							print(F'Generating {fsmt_tokenizer_config_file}')
							with open(_lowerCamelCase		,    "w"		,    encoding="utf-8") as f:
														f.write(json.dumps(_lowerCamelCase		,    ensure_ascii=_lowerCamelCase		,    indent=_lowerCamelCase))

							# model
							__UpperCamelCase      :  List[Any]       =   chkpt["models"][0]
							__UpperCamelCase      :  Tuple       =   model.state_dict()

							# rename keys to start with 'model.'
							__UpperCamelCase      :  Union[str, Any]       =   OrderedDict(("model." + k, v) for k, v in model_state_dict.items())

							# remove unneeded keys
							__UpperCamelCase      :  Dict       =   [
							    "model.model",
							    "model.encoder.version",
							    "model.decoder.version",
							    "model.encoder_embed_tokens.weight",
							    "model.decoder_embed_tokens.weight",
							    "model.encoder.embed_positions._float_tensor",
							    "model.decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														model_state_dict.pop(_lowerCamelCase		,    _lowerCamelCase)

							__UpperCamelCase      :  List[str]       =   FSMTConfig.from_pretrained(_lowerCamelCase)
							__UpperCamelCase      :  List[Any]       =   FSMTForConditionalGeneration(_lowerCamelCase)

							# check that it loads ok
							model_new.load_state_dict(_lowerCamelCase		,    strict=_lowerCamelCase)

							# save
							__UpperCamelCase      :  int       =   os.path.join(_lowerCamelCase		,    _lowerCamelCase)
							print(F'Generating {pytorch_weights_dump_path}')
							torch.save(_lowerCamelCase		,    _lowerCamelCase)

							print("Conversion is done!")
							print("\nLast step is to upload the files to s3")
							print(F'cd {data_root}')
							print(F'transformers-cli upload {model_dir}')


if __name__ == "__main__":
		lowercase    :   Optional[Any]						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--fsmt_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
		        ' bpecodes, etc.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase    :   Tuple						=			parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)